# main.py import spaces import torch import torch.nn.functional as F from torch.nn import DataParallel from torch import Tensor from transformers import AutoTokenizer, AutoModel import threading import queue import os import json import numpy as np import gradio as gr from huggingface_hub import InferenceClient import openai from openai import OpenAI from globalvars import API_BASE, intention_prompt, tasks , system_message, model_name from dotenv import load_dotenv import re from utils import load_env_variables import chromadb from chromadb import Documents, EmbeddingFunction, Embeddings from chromadb.config import Settings from chromadb import HttpClient from langchain_community.document_loaders import UnstructuredFileLoader from utils import load_env_variables , parse_and_route os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30' os.environ['CUDA_LAUNCH_BLOCKING'] = '1' os.environ['CUDA_CACHE_DISABLE'] = '1' device = torch.device("cuda" if torch.cuda.is_available() else "cpu") hf_token, yi_token = load_env_variables() def clear_cuda_cache(): torch.cuda.empty_cache() client = OpenAI( api_key=yi_token, base_url=API_BASE ) class EmbeddingGenerator: def __init__(self, model_name: str, token: str, intention_client): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=token, trust_remote_code=True) self.model = AutoModel.from_pretrained(model_name, token=token, trust_remote_code=True).to(self.device) self.intention_client = intention_client def clear_cuda_cache(self): torch.cuda.empty_cache() @spaces.GPU def compute_embeddings(self, input_text: str): # Get the intention intention_completion = self.intention_client.chat.completions.create( model="yi-large", messages=[ {"role": "system", "content": intention_prompt}, {"role": "user", "content": input_text} ] ) intention_output = intention_completion.choices[0].message['content'] # Parse and route the intention parsed_task = parse_and_route(intention_output) selected_task = list(parsed_task.keys())[0] # Construct the prompt try: task_description = tasks[selected_task] except KeyError: print(f"Selected task not found: {selected_task}") return f"Error: Task '{selected_task}' not found. Please select a valid task." query_prefix = f"Instruct: {task_description}\nQuery: " queries = [input_text] # Get the embeddings with torch.no_grad(): inputs = self.tokenizer(queries, return_tensors='pt', padding=True, truncation=True, max_length=4096).to(self.device) outputs = self.model(**inputs) query_embeddings = outputs.last_hidden_state.mean(dim=1) # Normalize embeddings query_embeddings = F.normalize(query_embeddings, p=2, dim=1) embeddings_list = query_embeddings.detach().cpu().numpy().tolist() self.clear_cuda_cache() return embeddings_list class MyEmbeddingFunction(EmbeddingFunction): def __init__(self, embedding_generator: EmbeddingGenerator): self.embedding_generator = embedding_generator def __call__(self, input: Documents) -> Embeddings: embeddings = [self.embedding_generator.compute_embeddings(doc) for doc in input] embeddings = [item for sublist in embeddings for item in sublist] return embeddings # main.py import os import uuid import gradio as gr import torch import torch.nn.functional as F from torch.nn import DataParallel from torch import Tensor from transformers import AutoTokenizer, AutoModel from huggingface_hub import InferenceClient from openai import OpenAI from langchain_community.document_loaders import UnstructuredFileLoader from chromadb import Documents, EmbeddingFunction, Embeddings from chromadb.config import Settings from chromadb import HttpClient from langchain_chroma import Chroma from utils import load_env_variables, parse_and_route from globalvars import API_BASE, intention_prompt, tasks, system_message, model_name from langchain_core.embeddings import Embeddings from chromadb.api.types import EmbeddingFunction, Documents os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30' os.environ['CUDA_LAUNCH_BLOCKING'] = '1' os.environ['CUDA_CACHE_DISABLE'] = '1' device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ### Utils hf_token, yi_token = load_env_variables() def clear_cuda_cache(): torch.cuda.empty_cache() client = OpenAI(api_key=yi_token, base_url=API_BASE) class EmbeddingGenerator: def __init__(self, model_name: str, token: str, intention_client): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=token, trust_remote_code=True) self.model = AutoModel.from_pretrained(model_name, token=token, trust_remote_code=True).to(self.device) self.intention_client = intention_client def clear_cuda_cache(self): torch.cuda.empty_cache() @spaces.GPU def compute_embeddings(self, input_text: str): # Get the intention intention_completion = self.intention_client.chat.completions.create( model="yi-large", messages=[ {"role": "system", "content": intention_prompt}, {"role": "user", "content": input_text} ] ) intention_output = intention_completion.choices[0].message['content'] # Parse and route the intention parsed_task = parse_and_route(intention_output) selected_task = list(parsed_task.keys())[0] # Construct the prompt try: task_description = tasks[selected_task] except KeyError: print(f"Selected task not found: {selected_task}") return f"Error: Task '{selected_task}' not found. Please select a valid task." query_prefix = f"Instruct: {task_description}\nQuery: " queries = [input_text] # Get the embeddings with torch.no_grad(): inputs = self.tokenizer(queries, return_tensors='pt', padding=True, truncation=True, max_length=4096).to(self.device) outputs = self.model(**inputs) query_embeddings = outputs.last_hidden_state.mean(dim=1) # Normalize embeddings query_embeddings = F.normalize(query_embeddings, p=2, dim=1) embeddings_list = query_embeddings.detach().cpu().numpy().tolist() self.clear_cuda_cache() return embeddings_list class ChromaEmbeddingsAdapter(Embeddings): def __init__(self, ef: EmbeddingFunction): self.ef = ef def embed_documents(self, texts): return self.ef(texts) def embed_query(self, query): return self.ef([query])[0] class LangChainEmbeddingAdapter(EmbeddingFunction[Documents]): def __init__(self, ef: Embeddings): self.ef = ef def __call__(self, input: Documents) -> Embeddings: return self.ef.embed_documents(input) class MyEmbeddingFunction(EmbeddingFunction): def __init__(self, embedding_generator: EmbeddingGenerator): self.embedding_generator = embedding_generator def __call__(self, input: Documents) -> Embeddings: embeddings = [self.embedding_generator.compute_embeddings(doc) for doc in input] embeddings = [item for sublist in embeddings for item in sublist] return embeddings def load_documents(file_path: str, mode: str = "elements"): loader = UnstructuredFileLoader(file_path, mode=mode) docs = loader.load() return [doc.page_content for doc in docs] def initialize_chroma(collection_name: str, embedding_function: MyEmbeddingFunction): client = Chroma.from_documents([], ChromaEmbeddingsAdapter(embedding_function)) # Initialize with no documents return client def add_documents_to_chroma(client, documents: list, embedding_function: MyEmbeddingFunction): for doc in documents: client.add(ids=[str(uuid.uuid1())], documents=[doc], embeddings=embedding_function([doc])) def query_chroma(client, query_text: str): result_docs = client.similarity_search(query_text) return result_docs # Initialize clients intention_client = OpenAI(api_key=yi_token, base_url=API_BASE) embedding_generator = EmbeddingGenerator(model_name=model_name, token=hf_token, intention_client=intention_client) embedding_function = MyEmbeddingFunction(embedding_generator=embedding_generator) chroma_client = initialize_chroma(collection_name="Tonic-instruct", embedding_function=embedding_function) def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): retrieved_text = query_documents(message) messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": f"{retrieved_text}\n\n{message}"}) response = "" for message in intention_client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message.choices[0].delta.content response += token yield response def upload_documents(files): for file in files: loader = UnstructuredFileLoader(file.name) documents = loader.load() add_documents_to_chroma(chroma_client, documents, embedding_function) return "Documents uploaded and processed successfully!" def query_documents(query): results = query_chroma(chroma_client, query) return "\n\n".join([result.page_content for result in results]) with gr.Blocks() as demo: with gr.Tab("Upload Documents"): with gr.Row(): document_upload = gr.File(file_count="multiple", file_types=["document"]) upload_button = gr.Button("Upload and Process") upload_button.click(upload_documents, inputs=document_upload, outputs=gr.Text()) with gr.Tab("Ask Questions"): with gr.Row(): chat_interface = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.", label="System message"), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), ], ) query_input = gr.Textbox(label="Query") query_button = gr.Button("Query") query_output = gr.Textbox() query_button.click(query_documents, inputs=query_input, outputs=query_output) if __name__ == "__main__": demo.launch()